117 results about "Row vector" patented technology

The invention provides a vehicle fault root cause diagnosis method, equipment, equipment and a medium, and the method comprises the steps: obtaining a vehicle target fault phenomenon, determining a to-be-detected fault event set based on a preset fault logic relation, constructing an initial scoring matrix, enabling a row vector to correspond to a fault event, enabling a column vector to correspond to the state feature parameters of a plurality of evaluation dimensions, and carrying out the detection of the fault event set; executing the detection task of the highest comprehensive score fault event, obtaining feedback data, updating the initial score matrix parameters based on the feedback data to obtain an updated score matrix, and calculating the comprehensive score of each event based on the updated score matrix again. Iteratively executing the detection task corresponding to the updated highest score event to identify whether the detection event is a fault root cause or not until the fault root cause of the target fault phenomenon is determined; according to the method, through a dynamic priority scheduling mechanism, real-time feedback data is fused in multi-dimensional evaluation, a high-value diagnosis task is executed preferentially, the resource consumption of traditional traversal diagnosis is remarkably reduced, and the fault positioning efficiency is effectively improved.

The invention relates to the field of wastewater treatment, in particular to an online analysis method and system for ink-jet printing wastewater treatment data, and the method comprises the steps: obtaining the water quality, process and equipment state parameter data of ink-jet printing wastewater based on each sensor, and constructing an original data matrix through preprocessing; decomposing the matrix, selecting a preset number of principal components for reconstruction, and generating a reconstructed data matrix; a reconstruction error is calculated by analyzing the difference between an original matrix and a reconstruction matrix, a correlation distortion index is analyzed based on row vector similarity, and an objective function is constructed to determine the number of optimal principal components; and clustering is carried out by using reconstruction errors and distances, abnormal data points are screened, and process parameters are regulated and controlled. According to the method, by optimizing the number of principal components and improving clustering analysis, the noise interference problem under complex water quality changes is effectively solved, the anomaly detection precision and the system stability are remarkably improved, and the robustness and the adaptability of an ink-jet printing wastewater treatment system are enhanced.

The invention provides an SAR (Synthetic Aperture Radar) anti-interference evaluation method and device based on maximum variance combined with layered entropy weight, and relates to the technical field of radar anti-interference. Comprising the following steps: constructing an evaluation index matrix according to a plurality of target SAR echoes; dividing the plurality of evaluation indexes into two layers of indexes by using a Pearson correlation analysis algorithm according to the evaluation index matrix and a preset threshold value; according to the plurality of evaluation indexes, the random number and the normalized evaluation index matrix, constructing a maximum variance evaluation optimization model which takes a maximized weighted evaluation matrix and a utility value matrix as objective functions and takes a difference degree weight row vector as a parameter to be solved; according to the maximum variance evaluation optimization model and the evaluation index matrix, determining a maximum difference evaluation matrix by using an interior point method; and according to the maximum difference evaluation matrix, the evaluation index matrix and the two layers of indexes, determining a plurality of evaluation values corresponding to the plurality of anti-interference methods by using a layered entropy weight method. In this way, the accuracy of anti-interference evaluation of various anti-interference methods is high.

The invention provides a game behavior anomaly detection method based on spatio-temporal feature fusion, which comprises the following steps of: visual coding of behavior data: collecting user operation time sequence data, and performing hue mapping coding according to event types, respectively mapping the basic operation event, the core interaction event, the numerical value change event and the non-operation event into differentiated colors, wherein the numerical value event adopts a gradient color to represent the change intensity; dividing the time sequence data into row vectors based on a preset time unit, generating a two-dimensional behavior map through multi-row stacking, transversely representing time continuity, longitudinally reflecting behavior cycle regularity, and forming a visual data carrier containing spatial-temporal characteristics; and two-channel detection model construction: constructing a double-layer architecture of a supervised detection channel and an enhanced detection channel, wherein the supervised detection channel adopts a deep convolutional neural network to extract map spatial features and is connected with an interpretable classifier to output a behavior compliance probability.

The invention discloses an infrared image vertical stripe removing method based on dynamic adjustment of vertical stripe distribution. The method comprises the following steps: (1) a high-dimensional feature vector matrix extraction step, and (2) a vector parameter extraction step: inputting the high-dimensional feature vector matrix and each frame of to-be-processed infrared image into a vertical line removing parameter generation network together, and generating two column vector parameters and two row vector parameters; 3) an improved traditional vertical stripe removing algorithm processing step which comprises the steps of performing Gaussian filtering on the infrared image to be processed in the row direction, calculating vertical stripe noise column by column, judging whether the vertical stripe noise is real vertical stripe noise column by column, obtaining a vertical stripe noise image of the image to be processed, and then performing subtraction to obtain an infrared image after vertical stripe removal; according to the method, a proper loss function and a proper training process are designed for the vertical stripe distribution information extraction network and the vertical stripe removal parameter generation network, so that the model achieves good performance, the distribution condition of vertical stripes can be effectively analyzed, and robust vertical stripe removal parameters are generated.

The invention provides a water supply network water leakage abnormity identification method based on NMF dimension reduction, and relates to the technical field of water supply network abnormity identification, and the method comprises the steps: embedding hydrophone sample data containing expert labeling information into a sample weight matrix as a row vector, and constructing a sample dimension loss item; hydrophone sample data containing industry feature priori are mapped to the feature basis matrix to serve as rows, and feature dimension loss items are constructed; combining the two loss items to construct a total loss function and solving the total loss function to obtain an optimal sample weight matrix; and performing clustering analysis based on the optimal sample weight matrix to obtain a clustering affiliation result of the hydrophone sample data so as to judge whether the water supply network to be identified has water leakage abnormity or not. According to the method, the double-constraint NMF framework fusing the expert knowledge of the sample dimension and the industry priori knowledge of the feature dimension is constructed, the accuracy and interpretability of hydrophone feature dimension reduction are considered, and the problems that an existing method is poor in interpretability, less in priori knowledge utilization and the like are solved.

The invention relates to the technical field of robot control, and discloses a robot control method and system based on multi-modal fusion, and the method comprises the steps: obtaining multi-modal sensing data, extracting features, and carrying out the clustering, thereby obtaining an associated information cluster sequence; a cluster center vector is extracted and quantized, and a quantitative evaluation matrix is obtained; calculating a covariance between matrix row vectors, and if the covariance is greater than zero, marking the covariance as a positive correlation coordination component to obtain a coordination component set; grouping the coordination component set, and if the number of the components in the group is less than a threshold value, merging adjacent groups; selecting a central group and predicting a change trend, and optimizing robot parameters after evaluating a risk value according to the change trend; and optimizing the updated parameters according to the multi-modal sensing data and a preset updating amplitude, and if the difference between the parameters before and after updating exceeds a threshold value, reducing the updating amplitude, and readjusting the parameters to obtain a final decision model. According to the method, the problem of low robot group cooperation efficiency in a complex scene can be solved.

The invention discloses an LDPC (Low Density Parity Check) code check matrix construction method, which is used for processing a low earth orbit satellite ground foundation zone, and comprises the following steps of: A1, determining degree distribution of a basis matrix under a high code rate by adopting an extrinsic information transfer graph EXIT, and realizing construction of the basis matrix under the constraint of optimal degree distribution through extrinsic information transfer P-EXIT based on a basic model graph; a2, based on the code length and the code rate of FEC coding of the DVB-S2 protocol, determining the size and the expansion factor of a basis matrix corresponding to each code rate; a3, determining a finite field and a generator based on an expansion factor, and constructing a linearly independent shift value row vector and cycle coefficient table; a4, selecting row vectors from the cyclic coefficient table to construct a cyclic shift matrix, and performing short ring identification and elimination on the cyclic shift matrix; and A5, performing matrix hashing on the basis of the basis matrix and the cyclic shift matrix to obtain an LDPC check matrix.

The invention belongs to the technical field of defect detection, and particularly relates to a defect detection method and system in a filter assembly laminating process based on image processing, so as to solve the technical problems that an existing detection method model is relatively weak in micro defect sensing capability and relatively low in detection accuracy. The detection method comprises the following steps: S1, carrying out block attention calculation on an image block sequence; s2, calculating an adjustment coefficient and adjusting the updating process of the row normalization factor by using the adjustment coefficient; s3, weighting corresponding row vectors in the initial context feature matrix by using the adjustment coefficient corresponding to each query block to obtain a weighted context feature matrix; and S4, outputting a mask pattern representing the position of the defect on the surface of the filter assembly. According to the detection method provided by the invention, secondary weighting and feature fusion are carried out on the initial context features, so that the accuracy of defect detection of the filter assembly is improved.

The invention provides a matrix multiplication calculation method and device based on a three-dimensional accelerated calculation architecture, and the method comprises the steps: segmenting a quantized target matrix into bit slice matrixes along a bit dimension, and carrying out the clustering of the bit slice matrixes along a row vector dimension, and obtaining a clustering center matrix and a class residual matrix; combining repeated operation between different columns of the clustering center matrix to obtain an intermediate activation vector; multiplying the enumeration matrix by the intermediate activation vector to reconstruct a calculation result of the clustering center; multiplying the similar residual matrix by the input activation, and adding with the calculation result of the corresponding clustering center to obtain the calculation result of the similar residual matrix; and obtaining a multiplication operation result of the target matrix and the input activation based on calculation results of all the clustering centers and the class residual matrix. According to the method, through joint optimization of three dimensions of bit, row and column, the redundancy calculation is minimized, the number of necessary operations is effectively reduced, and the complexity and calculation overhead of large-scale matrix multiplication are reduced.

A device for optical matrix-vector multiplication is provided. A x → = y → , encompassing: n * m optically coupled cells (i,j) (330, 331-399) representing the elements of an (n×m) matrix A, an input vector x → , whose components x i (221,222,223) are represented by n waveguides which are coupled to the rows of matrix A, forming an output vector y → whose components y j (231,232,233) are represented by m waveguides coupled to the columns of matrix A, wherein each cell (i,j) (330, 331-399) comprises an optical amplifier (400) configured to apply a weighting factor a ij to implement ∈[1;∞[.

The application discloses a new correlation image recognition and machine learning method based on fuzzy monotony, and belongs to the technical field of artificial intelligence of pattern recognition and machine learning; the method is defined as FMMCA, which evaluates local fuzzy monotone correlation by comparing row vectors and column vectors of an image matrix pair by pair, then the local correlations are weighted and accumulated, and finally the global fuzzy monotone correlation between images is obtained. The application directly uses fuzzy monotone correlation analysis to replace classical correlation analysis for multi-view research, so that the problems existing in classical correlation analysis do not exist, and the fuzzy monotone method feature does not need to be measured statically by distance, but can be measured dynamically by interval change, so that the influence of some noise is offset, the performance is improved, a new fuzzy monotone machine learning method is formed, the optimization of a focus loss function is not needed, the parameters are few, the robustness is good, and the computing power is small.

The application discloses a flexible DC transmission line intra-zone and extra-zone fault discrimination method based on S transform, a method of waveform continuation is used to cover the subsequent wave with the wave tail of the first wave, the influence of the subsequent wave head on the frequency characteristics is reduced, the S transform is used to make the modulus time frequency matrix of the continued waveform, the row vectors of the modulus time frequency matrix are integrated, the corresponding amplitude-frequency curve is obtained, the energy operator is used to weight the amplitude-frequency curve to improve the proportion of high-frequency components, the energy accumulation of different frequency bands is constructed, the ratio of the high-frequency band energy accumulation and the total frequency band energy accumulation is taken as the intra-zone and extra-zone fault recognition criterion, when the ratio is greater than the threshold value, the intra-zone fault is determined, otherwise, the extra-zone fault is determined. The method meets the rapidity requirement of the flexible DC transmission line protection, has high sensitivity, and has important reference value for the development of the flexible DC transmission protection.

The invention relates to the technical field of electrical digital data processing, and discloses a Web application automatic generation and data processing method based on spreadsheet mapping, which comprises the following steps: mapping spreadsheet cells into enumeration values to construct a type fingerprint matrix; calculating a type purity index of a column vector, identifying a vertical blocking band, and splitting the matrix into a plurality of orthogonal sub-matrixes which are not communicated with each other; calculating a type distribution difference value of adjacent row vectors in the sub-matrix to determine a horizontal logic partition line so as to define a logic data block; a Web application object model is assembled based on an arrangement mode of logic data blocks, signal crosstalk between transverse side-by-side data entities is cut off by introducing a vertical topology decoupling mechanism based on column entropy, and the technical problem that downlink feature scanning is easily interfered by orthogonal noise in a multi-table mixed arrangement scene is solved. And the deterministic structure reconstruction of the complex two-dimensional heterogeneous data under the condition of no preset template is realized.

The invention provides a bearing fault identification method based on singular value decomposition. The method comprises the following steps: acquiring a discrete vibration signal of a bearing; a Hankel matrix of the vibration signals is constructed, the dimension of the matrix is smaller than < and is the length of the vibration signals, and the column number is determined based on the minimum characteristic frequency of typical components of the equipment; performing singular value decomposition on the matrix; selecting an effective row vector of the matrix by taking high periodicity, high cyclic stability and low complexity of the signal as targets, determining an effective singular value based on the effective row vector, and performing signal reconstruction based on the effective singular value; and carrying out spectrum analysis on the reconstructed signal and carrying out bearing fault identification according to a relation between a prominent frequency component in a spectrum and a bearing fault characteristic frequency. According to the bearing fault identification method, the features of the signals can be enhanced while noise reduction is carried out on the signals, and accurate judgment of bearing faults is facilitated.

The invention provides a large language model binarization quantification method and system based on instructive alternate optimization, and relates to the technical field of large language model deploying.The method comprises the steps that a weight matrix is divided into an important area and an unimportant area by evaluating the influence degree of all weight parameters on the performance of a large language model; carrying out binaryzation on all areas of the weight matrix, and alternately optimizing a row vector scaling factor and a column vector scaling factor by adopting a first-order row-column alternate optimization iteration mode to obtain a first-order reconstruction weight matrix; and carrying out binaryzation again on the important region of the first-order reconstruction weight matrix, carrying out optimization by adopting a first-order and second-order row-column alternating optimization iteration mode to obtain a second-order reconstruction weight matrix, and correspondingly taking the second-order reconstruction weight matrix as a weight parameter after the large language model is quantized. The quantization error of the key weight parameter can be effectively reduced, the quantization precision is high, the quantization process completely depends on the internal structure information of the large language model, and the edge deployment compatibility is high.

A push information determination method, apparatus, medium, device and program product, the push information determination method comprising: acquiring position information of a target position and respective position information of a plurality of first objects, the position information comprising three-dimensional space coordinates under an earth coordinate system; a first matrix is constructed according to the position information of the target position, a second matrix is constructed according to the respective position information of the multiple first objects, and the number of row vectors in the second matrix is the same as that of the first objects; determining a preset number of target objects closest to the target position from the plurality of first objects according to the first matrix and the second matrix; and determining to-be-pushed information according to the information of the target object. Through the above technical scheme, the first matrix and the second matrix are constructed, and the plurality of first objects are sorted through the matrix-form operation, so that the calculation efficiency is higher, the resource occupation amount is less, and the accuracy of the to-be-pushed information is improved.

The application relates to the technical field of electric digital data processing, and discloses a Web application automatic generation and data processing method based on electronic table mapping, which comprises the following steps: mapping electronic table cells into enumeration values to construct a type fingerprint matrix; calculating a type purity index of a column vector and identifying a vertical blocking band, and splitting the matrix into a plurality of mutually unconnected orthogonal sub-matrices; calculating a type distribution difference value of adjacent row vectors in the sub-matrices to determine a horizontal logical segmentation line, so as to define a logical data block; and assembling a Web application object model based on an arrangement mode of the logical data block. The application introduces a vertical topology decoupling mechanism based on column entropy, cuts off signal crosstalk between horizontally arranged data entities, solves the technical problem that a horizontal feature scanning in a multi-table mixed arrangement scene is easily interfered by orthogonal noise, and realizes deterministic structure reconstruction of complex two-dimensional heterogeneous data under the condition of no preset template.

The application discloses a model training method and device and a recommendation method of an operator package. The method comprises the following steps: obtaining historical operator package data and historical voice data of a target object; generating a first row vector corresponding to the historical operator package data, performing sentiment analysis on the historical voice data, and obtaining a second row vector; merging the first row vector and the second row vector to obtain a target matrix; inputting the target matrix into a generative adversarial network model; determining a sentiment tendency reward according to the first row vector and the second row vector; determining a target probability as an optimization parameter, updating the optimization parameter, and obtaining a trained generative adversarial network model if a loss function related to the target probability meets a preset convergence condition. The application solves the technical problem of low recommendation accuracy of an operator package caused by the fact that a related operator package recommendation method is limited by a static model and does not fully consider the dynamic change of user demand.

The invention provides a data intelligent analysis method and system applied to carbon emission monitoring, and the method comprises the following steps: executing an NMF load decoupling algorithm to obtain a dynamic non-negative vector which comprises a resident coefficient and an industrial coefficient; according to the non-negative row vector and the space-time database, respectively calculating resident and industrial carbon emission intensities corresponding to the current time period, and updating the carbon emission intensities to the space-time database; calculating and generating future carbon emission predicted values corresponding to different future time periods according to the space-time database; and performing anomaly detection calculation according to the carbon emission intensity and a future carbon emission predicted value corresponding to the current time period. By adopting the method, the carbon emission intensity condition of the whole area can be intuitively and indirectly reflected in real time without tampering.

A kind of GNSS data processing method based on single observation filtering, comprising the following steps: first, design matrix, observation vector and covariance matrix are decomposed into multiple row vectors or scalar, then one observation is processed each time, after all the observations of current epoch are processed through multiple cycles, the state vector and covariance matrix of current epoch recursive update are obtained;GNSS parameters in state vector are divided into common parameters and non-common parameters, when processing each observation, update common parameters, then gradually increase new parameters in state vector, i.e. process observation by observation, to obtain extended state vector and covariance matrix;In the third step, when data processing is carried out using the above-mentioned extended state vector and covariance matrix, it includes single epoch processing and multi-epoch processing;The state estimation of current epoch is output as the result of GNSS positioning.The present application has high efficiency when processing high-dimensional observation data.

Various embodiments of the present disclosure are directed to a photonic circuit for a vector-matrix operation. A source pixel is configured to generate a light beam. An optical fan-out structure is configured to generate a plurality of copies of the light beam. A plurality of modulator pixels are configured to respectively transmit the plurality of copies with individual transmissivities to generate a plurality of transmitted light beams. A plurality of detector pixels are configured to accumulate charge respectively in response to the plurality of transmitted light beams. A controller is configured to control the source pixel and the plurality of modulator pixels to modulate an intensity of the light beam and the individual transmissivities to perform the vector-matrix multiplication operation. The intensity is modulated to temporally encode an input row vector, and the individual transmissivities are modulated to temporally encode corresponding column vectors of a weight matrix.

The application provides a modulation method and system of an orthogonal frequency division multiplexing signal, and belongs to the technical field of wireless communication. The method comprises the following steps: mapping a frequency domain data signal and a pilot signal to be transmitted into a frequency domain resource grid matrix; performing a frequency domain shift operation on each row vector of the frequency domain resource grid matrix, shifting a zero frequency component to the center of the vector, and obtaining a shifted matrix; and performing an inverse fast Fourier transform on each row vector of the shifted matrix, and obtaining a time domain orthogonal frequency division multiplexing symbol matrix as a modulation result. The application constructs the entire frequency domain resource into a two-dimensional matrix, and all subsequent signal processing operations are based on the row vectors of the two-dimensional matrix as a basic unit, and are completed through a series of explicit matrix and vector operation steps. Thus, the IFFT call for the entire frame, which is a black box, is decomposed into individual steps for a single symbol, which are visible and controllable, and the problem of opaque process is solved.

The present disclosure provides a recommendation model training method, a recommendation method, a device, an electronic device, a storage medium and a program product. The recommendation model training method comprises: obtaining a sample set, wherein the sample set comprises a plurality of sample users and a plurality of sample items; constructing an initial relationship matrix based on the sample set, wherein one of the row vector and the column vector of the initial relationship matrix is used as a user vector, and the other is used as an item vector; reconstructing the initial relationship matrix using a to-be-trained debiasing network to obtain a reconstructed relationship matrix; decoupling the reconstructed relationship matrix into a causal matrix and a bias matrix, determining a plurality of positive samples according to the causal matrix and the bias matrix, and determining a plurality of negative samples according to the initial relationship matrix, wherein each sample comprises a sample user and a sample item; using the plurality of positive samples and the plurality of negative samples as training samples to train the to-be-trained debiasing network and the to-be-trained recommendation model to obtain the debiasing network and the recommendation model.

This invention proposes a game behavior anomaly detection method based on spatiotemporal feature fusion. The method includes the following steps: Visual encoding of behavior data: Collecting user operation time-series data, encoding it by hue mapping according to event type, mapping basic operation events, core interaction events, numerical change events, and no-operation events to differentiated colors, where numerical events use gradient colors to represent the intensity of change; Dividing the time-series data into row vectors based on preset time units, generating a two-dimensional behavior map by stacking multiple rows, representing temporal continuity horizontally and reflecting the periodic regularity of behavior vertically, forming a visual data carrier containing spatiotemporal features; Construction of a dual-channel detection model: Constructing a two-layer architecture of a supervised detection channel and an enhanced detection channel. The supervised detection channel uses a deep convolutional neural network to extract spatial features of the map and connects to an interpretable classifier to output the behavior compliance probability.

The present disclosure relates to the technical field of intelligent parameter adjustment, and in particular, to a database adaptive parameter adjustment method, device, equipment, medium and product. The method comprises: taking an adjusted known database instance as a standard instance according to a user's business state. Obtaining instance information of a to-be-adjusted database instance and business information of the user. Vectorizing the instance information of the standard instance, the instance information of the to-be-adjusted database instance and the business information, and obtaining a target vector matrix after processing. Judging whether each column vector in the target vector matrix is derived from a source domain or a target domain, and obtaining a judgment result. The source domain is a data distribution corresponding to the standard instance, and the target domain is a data distribution corresponding to the to-be-adjusted database instance. Based on the judgment result, the to-be-adjusted database is adaptively parameter-adjusted. The present disclosure adjusts one standard instance as a benchmark by a domain expert, so that the parameter configuration of the to-be-adjusted database instance continuously approaches the standard instance, and the accuracy of the parameter adjustment process is improved.

The present application relates to the field of wastewater treatment, and more particularly to a method and system for online analysis of inkjet printing wastewater treatment data, the method comprising: obtaining water quality, process and equipment state parameter data of inkjet printing wastewater based on each sensor, and constructing an original data matrix after preprocessing; decomposing the matrix, selecting a preset principal component number for reconstruction, and generating a reconstructed data matrix; calculating reconstruction error by analyzing the difference between the original and reconstructed matrices, analyzing the correlation distortion index based on row vector similarity, constructing a target function to determine the optimal principal component number; and using reconstruction error and distance for clustering, filtering abnormal data points and adjusting process parameters. The present application effectively solves the noise interference problem under complex water quality changes by optimizing the principal component number and improving the clustering analysis, significantly improves the abnormal detection accuracy and system stability, and enhances the robustness and adaptability of the inkjet printing wastewater treatment system.

The invention relates to a matrix operation method, a matrix operation device, computer equipment, a storage medium and a program product. The method comprises the steps that in response to operation instructions of a first matrix and a second matrix, matrix blocks are distributed to threads needing to be called by a heterogeneous accelerator in the heterogeneous computing system; and sending an operation instruction to the heterogeneous accelerator, and indicating each thread in the heterogeneous accelerator to operate the distributed matrix blocks in parallel. Wherein each matrix block comprises at least one first matrix row vector and at least one second matrix column vector. By adopting the method, the matrix operation efficiency can be improved.

This invention discloses a method and system for optimizing the distribution of measurement points during dynamic load identification. The method includes: determining the number of measurement points K and the modal cutoff number N; selecting an initial value x1 for the natural mode P = Max(K,N) before solving; and solving the equation to examine whether the equation has P-1 solutions x2, ..., x P If not, the initial value x1 is changed and the solution is solved again until there are P-1 solutions in the solution set. It is then verified whether the initial value and the P-1 solutions ensure that the matrix row vectors are orthogonal and their magnitudes are approximately similar. Based on the P solutions of the equation that satisfy the verification conditions, the measurement point that maximizes load identification stability is obtained. This invention constructs a measurement point location optimization method with the goal of maximizing the stability of the response-load equation. Compared to classical methods, this successfully reduces the ill-conditioned nature of the dynamic calibration matrix.

The invention provides a general interference suppression method and system based on adaptive window short-time Fourier transform, and the method comprises the steps: carrying out the adaptive initialization of a Gaussian window width parameter according to the amplitude standard deviation of each pulse signal, carrying out the short-time Fourier transform, obtaining a time-frequency matrix of each pulse, carrying out the vectorization, carrying out the arrangement according to columns, and obtaining a time-frequency matrix of each pulse; constructing a time frequency-pulse matrix; performing complex RPCA decomposition on the matrix, and separating a low-rank matrix and a sparse matrix; adjusting a Gaussian window width parameter according to energy distribution feedback of each column vector of the sparse matrix, and performing iterative optimization until convergence to obtain a final sparse matrix; extracting interference time-frequency column vectors, reconstructing the interference time-frequency column vectors into an interference time-frequency matrix, and performing inverse short-time Fourier transform to obtain a time-domain interference signal; and finally, subtracting the corresponding interference signal from the original echo signal. According to the method, various types of interferences can be effectively suppressed, and particularly, the suppression effect can be remarkably improved on the premise of protecting target signals when broadband interferences and complex deception interferences are handled.